無料PDF1z0-1122-24更新版 |最初の試行で簡単に勉強して試験に合格する &信頼できるOracle Oracle Cloud Infrastructure 2024 AI Foundations Associate

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Oracle 1z0-1122-24 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
トピック 2	Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
トピック 3	Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
トピック 4	Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
トピック 5	Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.

Oracle Cloud Infrastructure 2024 AI Foundations Associate 認定 1z0-1122-24 試験問題 (Q29-Q34):

質問 # 29
What is the primary purpose of reinforcement learning?

A. Finding relationships within data sets
B. Learning from outcomes to make decisions
C. Making predictions from labeled data
D. Identifying patterns in data

正解：B

解説：
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

質問 # 30
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Chat models
B. Embedding models
C. Translation models
D. Generation models

正解：C

解説：
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.

質問 # 31
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Chat models
B. Embedding models
C. Translation models
D. Generation models

正解：C

質問 # 32
What role do Transformers perform in Large Language Models (LLMs)?

A. Manually engineer features in the data before training the model
B. Image recognition tasks in LLMs
C. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
D. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints

正解：C

解説：
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.

質問 # 33
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Text processing
B. Time series prediction
C. Image classification
D. Image generation

正解：C

解説：
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

質問 # 34
......

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Biography

無料PDF1z0-1122-24更新版 |最初の試行で簡単に勉強して試験に合格する &信頼できるOracle Oracle Cloud Infrastructure 2024 AI Foundations Associate

1z0-1122-24最新試験 & 1z0-1122-24ミシュレーション問題

Oracle 1z0-1122-24 認定試験の出題範囲：

Oracle Cloud Infrastructure 2024 AI Foundations Associate 認定 1z0-1122-24 試験問題 (Q29-Q34):

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